南京大学学报（自然科学版）2013，Vol.49Issue(2)：142-149,8.
基于项目类别和兴趣度的协同过滤推荐算法
Collaborative filtering recommendation algorithm based on item category and interest
摘要
Abstract
Collaborative filtering-based recommender systems, which automatically predict preferred products of a user using known preferences of other users, have become extremely popular in recent years due to the increase in web-based activities such as e-commerce and online content distribution. However, traditional collaborative filtering techniques provide poor accuracy,a large number of ratings from similar users or similar items are not available,due to the sparsity inherent to rating data. Consequently,prediction quality can be poor. To address the matter,a new collaborative filtering recommendation algorithm based on item category and interest measure is proposed. In this algorithm, first,the item categories similarity matrix is constructed by calculating the item-item category distance, and then analyzes the correlation degree of different items by using Piatetsky-Shapiro interestingness measure, at last,a novel collaborative filtering algorithm is proposed after combining the information of item categories with item-item interestingness and utilizing ameliorated conditional probability method as the standard item-item similarity measure. Empirical evaluation of the algorithm on large movie rating datasets demonstrates that it is not only an effective solution to data sparisity and the drawbacks of traditional similarity method,but also improves the accuracy of user interest and nearest neighbor search. At the same time, this algorithm achieves better prediction accuracy compared to other well-performing collaborative filtering algorithms.关键词
推荐系统/协同过滤/项目相似性/项目类别相似性/项目兴趣度Key words
recommendation systems/ collaborative filtering/ item similarity/ item category similarity/ item interest measure引用本文复制引用
韦素云,业宁,吉根林,张丹丹,殷晓飞..基于项目类别和兴趣度的协同过滤推荐算法[J].南京大学学报（自然科学版）,2013,49(2):142-149,8.基金项目
国家"973"项目(2012CB114505),国家杰出青年基金(31125008),江苏省自然科学基金(BK2009393),江苏省青蓝工程(CXLX11_0525),南京林业大学科技创新项目(163070079),江苏高校大学生创新计划项目(164070742) （2012CB114505）
